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Feature Extraction of Ball Bearings in
Time-Space and Estimation of Fault Size
with Method of ANN Kaplan Kaplan1, Samet Bayram
2, Melih Kuncan
3, H.Metin Ertunç
4
Abstract Faults in bearings used in machines cause downtime
and leads to catastrophic results on the machining operations. In
this study, specific sizes of the artificial bearings defects are
created and vibration signals were obtained from a shaft-bearing
system. The purpose of this study is to diagnose the size of the
defects occurring in bearings by using Artificial Neural
Networks(ANN) model. Features of vibration data are extracted
in real time and are multiplied with specific weights; then they
were given as input to the ANN model. Statistical properties of
bearings faults are observed that their values vary depending on
fault dimensions in real-time. These features are examined by
using ANN and the size of the defects occurring in bearings are
classified with 100% success, on the other hand the prediction
permonfance of actual error for a ANN model is found 2%.
Keywords- Artificial neural networks; bearings; diagnosing
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A. The Test Platform
B. Raw Vibration Data and Feature Extraction
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C. Establishment of Artificial Neural Network Models
kurtosis
D. Simulation
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